Analysis of a person's behavior has been an important aspect that has a plurality of applications in the field of marketing, organizational development etc. Due to this the field of personal context analysis is gaining wide importance. Specifically organizations employing a large number of employees are concerned to analyze the behavior of an individual for faster and better growth of the organization. The increasing need of analysis in terms of personal context has led to increasing growth in the field of Organizational Behavior Analysis, Workspace Ergonomics Analysis, Discovering the user's physical interaction network, User Studies Analysis, Market Study and Real-time Usage capture etc.
A number of technologies are available in the market for analyzing social behavior of a person using the reality mining techniques and other related aspects such as work cultures which have dedicated software and hardware requirements of the system. Such systems analyze Organizational Behavior based on user context sensing via specially designed devices that incorporate all required sensors. These devices interact with each other and a server to gather information associated with an individual. However, such systems and devices pose a threat as data related to a number of individuals is required to be transmitted to a back-end server for further processing, thereby raising privacy concerns. Moreover, further refinement and data processing at a distant located server leads to heavy transmission costs. Further, such data transmission leads to extra usage of battery power in order to transmit each and every particular sensed detail to the back-end server without processing it.
Furthermore, an additional device is required to be deployed at an extra cost in order to track an individual's behavior. Such a device does not generally has an always-on connectivity to dump the user data collected to the back-end server for further analysis—it needs to be docked to a special data collection station to transfer the data. Also, such available devices do not have provision to connect to additional external sensors over wireless, so the extensibility of the system to newer applications is limited.
Also, current solutions for context recognition and analysis using the reality mining techniques are dependent on wearable sensors and mobile devices for sensing a user's activity, location and proximity with respect to other users. There are different algorithms used to arrive at the conclusion of a user's attributes in the real world. Such results are often inaccurate due to errors in sensor readings and change in ambient environment. Such inaccuracies can cause discrepancies and malfunctioning in the case of ubiquitous applications. Furthermore, the sensors used are very limited in the kind of data they provide and most of the time specialized sensors are needed for deployment.
As a result there is growing need to integrate a personal context analysis system with a more efficient, widely available and user-friendly device which is easy to carry and simple to operate, and thereby eliminating the need for a separate special device or data collection system. There is also a need to process the raw sensory data at the sensing device itself to preserve battery life of the device (radio communication takes up most of the battery life), and thereby also addressing the privacy preservation and data transmission cost concerns.
Moreover, a provision to connect to additional external sensors through existing communication means like USB, Wi-Fi or Bluetooth will also lead to better grasp of an individual's behavior. Further the personal context analysis will also lead to create a social network based on real life fixations and affinities of the user.